MINERVA-Cultural: A Benchmark for Cultural and Multilingual Long Video Reasoning

Darshan Singh; Arsha Nagrani; Kawshik Manikantan; Harman Singh; Dinesh Tewari; Tobias Weyand; Cordelia Schmid; Anelia Angelova; Shachi Dave

arXiv · 2601.10649

MINERVA-Cultural: A Benchmark for Cultural and Multilingual Long Video Reasoning

Darshan Singh·Arsha Nagrani·Kawshik Manikantan·Harman Singh·Dinesh Tewari·Tobias Weyand·Cordelia Schmid·Anelia Angelova·Shachi Dave

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Abstract

Recent advancements in video models have shown tremendous progress, particularly in long video understanding. However, current benchmarks predominantly feature western-centric data and English as the dominant language, introducing significant biases in evaluation. To address this, we introduce MINERVA-Cultural, a challenging benchmark for multicultural and multilingual video reasoning. MINERVA-Cultural comprises high-quality, entirely human-generated annotations from diverse, region-specific cultural videos across 18 global locales. Unlike prior work that relies on automatic translations, MINERVA-Cultural provides complex questions, answers, and multi-step reasoning steps, all crafted in native languages. Making progress on MINERVA-Cultural requires a deeply situated understanding of visual cultural context. Furthermore, we leverage MINERVA-Cultural's reasoning traces to construct evidence-based graphs and propose a novel iterative strategy using these graphs to identify fine-grained errors in reasoning. Our evaluations reveal that SoTA Video-LLMs struggle significantly, performing substantially below human-level accuracy, with errors primarily stemming from the visual perception of cultural elements. MINERVA-Cultural will be publicly available under https://github.com/google-deepmind/neptune?tab=readme-ov-file\#minerva-cultural

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Darshan Singh, Arsha Nagrani, Kawshik Manikantan, Harman Singh, Dinesh Tewari, Tobias Weyand, Cordelia Schmid, Anelia Angelova, Shachi Dave. 2026-01-15. MINERVA-Cultural: A Benchmark for Cultural and Multilingual Long Video Reasoning. https://arxiv.org/abs/2601.10649

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